Overview

Dataset statistics

Number of variables20
Number of observations8761
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory160.0 B

Variable types

Numeric19
Categorical1

Alerts

PO_entrada has constant value "4.6" Constant
Q_entrada is highly correlated with Q_saida and 5 other fieldsHigh correlation
NH_entrada is highly correlated with TN_entrada and 1 other fieldsHigh correlation
TSS_entrada is highly correlated with DQO_entrada and 11 other fieldsHigh correlation
DQO_entrada is highly correlated with TSS_entrada and 11 other fieldsHigh correlation
DBO_entrada is highly correlated with TSS_entrada and 11 other fieldsHigh correlation
TN_entrada is highly correlated with NH_entrada and 6 other fieldsHigh correlation
TKN_entrada is highly correlated with NH_entrada and 6 other fieldsHigh correlation
TP_entrada is highly correlated with TSS_entrada and 11 other fieldsHigh correlation
Q_saida is highly correlated with Q_entrada and 5 other fieldsHigh correlation
NH_saida is highly correlated with Q_entrada and 13 other fieldsHigh correlation
NO_saida is highly correlated with Q_entrada and 13 other fieldsHigh correlation
TSS_saida is highly correlated with Q_entrada and 9 other fieldsHigh correlation
DQO_saida is highly correlated with TSS_entrada and 13 other fieldsHigh correlation
DBO_saida is highly correlated with Q_entrada and 13 other fieldsHigh correlation
TN_saida is highly correlated with NH_saida and 7 other fieldsHigh correlation
TKN_saida is highly correlated with Q_entrada and 13 other fieldsHigh correlation
TP_saida is highly correlated with TSS_entrada and 11 other fieldsHigh correlation
PO_saida is highly correlated with TSS_entrada and 11 other fieldsHigh correlation
Q_entrada is highly correlated with Q_saida and 3 other fieldsHigh correlation
NH_entrada is highly correlated with TN_entrada and 1 other fieldsHigh correlation
TSS_entrada is highly correlated with DQO_entrada and 8 other fieldsHigh correlation
DQO_entrada is highly correlated with TSS_entrada and 8 other fieldsHigh correlation
DBO_entrada is highly correlated with TSS_entrada and 8 other fieldsHigh correlation
TN_entrada is highly correlated with NH_entrada and 6 other fieldsHigh correlation
TKN_entrada is highly correlated with NH_entrada and 6 other fieldsHigh correlation
TP_entrada is highly correlated with TSS_entrada and 8 other fieldsHigh correlation
Q_saida is highly correlated with Q_entrada and 3 other fieldsHigh correlation
NH_saida is highly correlated with Q_entrada and 8 other fieldsHigh correlation
NO_saida is highly correlated with TSS_saidaHigh correlation
TSS_saida is highly correlated with Q_entrada and 9 other fieldsHigh correlation
DQO_saida is highly correlated with TSS_entrada and 12 other fieldsHigh correlation
DBO_saida is highly correlated with TSS_entrada and 8 other fieldsHigh correlation
TN_saida is highly correlated with NH_saida and 6 other fieldsHigh correlation
TKN_saida is highly correlated with Q_entrada and 8 other fieldsHigh correlation
TP_saida is highly correlated with TSS_entrada and 9 other fieldsHigh correlation
PO_saida is highly correlated with TSS_entrada and 9 other fieldsHigh correlation
Q_entrada is highly correlated with Q_saida and 1 other fieldsHigh correlation
NH_entrada is highly correlated with TN_entrada and 1 other fieldsHigh correlation
TSS_entrada is highly correlated with DQO_entrada and 6 other fieldsHigh correlation
DQO_entrada is highly correlated with TSS_entrada and 6 other fieldsHigh correlation
DBO_entrada is highly correlated with TSS_entrada and 6 other fieldsHigh correlation
TN_entrada is highly correlated with NH_entrada and 5 other fieldsHigh correlation
TKN_entrada is highly correlated with NH_entrada and 5 other fieldsHigh correlation
TP_entrada is highly correlated with TSS_entrada and 6 other fieldsHigh correlation
Q_saida is highly correlated with Q_entrada and 1 other fieldsHigh correlation
NH_saida is highly correlated with NO_saida and 6 other fieldsHigh correlation
NO_saida is highly correlated with NH_saida and 6 other fieldsHigh correlation
TSS_saida is highly correlated with Q_entrada and 6 other fieldsHigh correlation
DQO_saida is highly correlated with TSS_entrada and 9 other fieldsHigh correlation
DBO_saida is highly correlated with NH_saida and 7 other fieldsHigh correlation
TN_saida is highly correlated with NH_saida and 4 other fieldsHigh correlation
TKN_saida is highly correlated with NH_saida and 6 other fieldsHigh correlation
TP_saida is highly correlated with DQO_saida and 2 other fieldsHigh correlation
PO_saida is highly correlated with TSS_entrada and 9 other fieldsHigh correlation
Dia is highly correlated with Q_entrada and 17 other fieldsHigh correlation
Q_entrada is highly correlated with Dia and 8 other fieldsHigh correlation
NH_entrada is highly correlated with Dia and 5 other fieldsHigh correlation
TSS_entrada is highly correlated with Dia and 14 other fieldsHigh correlation
DQO_entrada is highly correlated with Dia and 14 other fieldsHigh correlation
DBO_entrada is highly correlated with Dia and 14 other fieldsHigh correlation
TN_entrada is highly correlated with Dia and 14 other fieldsHigh correlation
TKN_entrada is highly correlated with Dia and 14 other fieldsHigh correlation
TP_entrada is highly correlated with Dia and 14 other fieldsHigh correlation
Q_saida is highly correlated with Dia and 8 other fieldsHigh correlation
NH_saida is highly correlated with Dia and 16 other fieldsHigh correlation
NO_saida is highly correlated with Dia and 13 other fieldsHigh correlation
TSS_saida is highly correlated with Dia and 16 other fieldsHigh correlation
DQO_saida is highly correlated with Dia and 14 other fieldsHigh correlation
DBO_saida is highly correlated with Dia and 11 other fieldsHigh correlation
TN_saida is highly correlated with Dia and 17 other fieldsHigh correlation
TKN_saida is highly correlated with Dia and 16 other fieldsHigh correlation
TP_saida is highly correlated with Dia and 16 other fieldsHigh correlation
PO_saida is highly correlated with Dia and 16 other fieldsHigh correlation
Dia is uniformly distributed Uniform
Dia has unique values Unique
Q_entrada has unique values Unique
TN_entrada has unique values Unique
TKN_entrada has unique values Unique
Q_saida has unique values Unique
NH_saida has unique values Unique
NO_saida has unique values Unique
TSS_saida has unique values Unique
DQO_saida has unique values Unique
DBO_saida has unique values Unique
TN_saida has unique values Unique
TKN_saida has unique values Unique
TP_saida has unique values Unique
PO_saida has unique values Unique

Reproduction

Analysis started2023-04-20 17:43:34.030838
Analysis finished2023-04-20 17:44:17.961366
Duration43.93 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Dia
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct8761
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.500146
Minimum0
Maximum365
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-04-20T14:44:18.073865image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.2500146
Q191.250073
median182.500146
Q3273.750219
95-th percentile346.7502774
Maximum365
Range365
Interquartile range (IQR)182.500146

Descriptive statistics

Standard deviation105.3845504
Coefficient of variation (CV)0.5774491294
Kurtosis-1.200000003
Mean182.500146
Median Absolute Deviation (MAD)91.250073
Skewness-1.897227445 × 10-9
Sum1598883.779
Variance11105.90346
MonotonicityStrictly increasing
2023-04-20T14:44:18.186178image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
243.45852811
 
< 0.1%
243.20852791
 
< 0.1%
243.25019461
 
< 0.1%
243.29186131
 
< 0.1%
243.3335281
 
< 0.1%
243.37519471
 
< 0.1%
243.41686141
 
< 0.1%
243.50019481
 
< 0.1%
245.25019621
 
< 0.1%
Other values (8751)8751
99.9%
ValueCountFrequency (%)
01
< 0.1%
0.04166671
< 0.1%
0.08333341
< 0.1%
0.12500011
< 0.1%
0.16666681
< 0.1%
0.20833351
< 0.1%
0.25000021
< 0.1%
0.29166691
< 0.1%
0.33333361
< 0.1%
0.37500031
< 0.1%
ValueCountFrequency (%)
3651
< 0.1%
364.95862531
< 0.1%
364.91695861
< 0.1%
364.87529191
< 0.1%
364.83362521
< 0.1%
364.79195851
< 0.1%
364.75029181
< 0.1%
364.70862511
< 0.1%
364.66695841
< 0.1%
364.62529171
< 0.1%

Q_entrada
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct8761
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7999.298009
Minimum2334.275029
Maximum13259.80614
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-04-20T14:44:18.307573image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2334.275029
5-th percentile5327.333162
Q16735.567663
median7944.034851
Q39298.460258
95-th percentile10790.81241
Maximum13259.80614
Range10925.53111
Interquartile range (IQR)2562.892596

Descriptive statistics

Standard deviation1691.747745
Coefficient of variation (CV)0.2114870259
Kurtosis-0.520394166
Mean7999.298009
Median Absolute Deviation (MAD)1293.958751
Skewness0.04568892419
Sum70081849.86
Variance2862010.434
MonotonicityNot monotonic
2023-04-20T14:44:18.423353image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47191
 
< 0.1%
8653.8808561
 
< 0.1%
8592.8808081
 
< 0.1%
8603.0474821
 
< 0.1%
8613.2141571
 
< 0.1%
8623.3808321
 
< 0.1%
8633.5475071
 
< 0.1%
8643.7141821
 
< 0.1%
8664.0475311
 
< 0.1%
8079.8129081
 
< 0.1%
Other values (8751)8751
99.9%
ValueCountFrequency (%)
2334.2750291
< 0.1%
2520.717361
< 0.1%
2644.3007921
< 0.1%
2767.8842251
< 0.1%
2891.4676571
< 0.1%
3015.0510891
< 0.1%
3138.6345211
< 0.1%
3184.8435331
< 0.1%
3262.2179531
< 0.1%
3374.8261221
< 0.1%
ValueCountFrequency (%)
13259.806141
< 0.1%
13121.417361
< 0.1%
13070.28371
< 0.1%
12981.042251
< 0.1%
12920.38721
< 0.1%
12878.283551
< 0.1%
12840.667131
< 0.1%
12819.718181
< 0.1%
12798.40351
< 0.1%
12718.21811
< 0.1%

NH_entrada
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7725
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.79111463
Minimum8.001116364
Maximum68.92796875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-04-20T14:44:18.541267image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum8.001116364
5-th percentile16.0834614
Q120.9167002
median24.5834186
Q328.6248397
95-th percentile33.8339516
Maximum68.92796875
Range60.92685238
Interquartile range (IQR)7.7081395

Descriptive statistics

Standard deviation5.870003097
Coefficient of variation (CV)0.2367785066
Kurtosis3.552512227
Mean24.79111463
Median Absolute Deviation (MAD)3.8346604
Skewness0.7134445239
Sum217194.9552
Variance34.45693636
MonotonicityNot monotonic
2023-04-20T14:44:18.654355image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24189
 
2.2%
25188
 
2.1%
22142
 
1.6%
1770
 
0.8%
2369
 
0.8%
2669
 
0.8%
3247
 
0.5%
3046
 
0.5%
3146
 
0.5%
2124
 
0.3%
Other values (7715)7871
89.8%
ValueCountFrequency (%)
8.0011163641
< 0.1%
8.08334781
< 0.1%
8.16668121
< 0.1%
8.25001461
< 0.1%
8.29161651
< 0.1%
8.3333481
< 0.1%
8.41668141
< 0.1%
8.50001481
< 0.1%
8.58328341
< 0.1%
8.58334821
< 0.1%
ValueCountFrequency (%)
68.927968751
< 0.1%
67.45187931
< 0.1%
67.42329261
< 0.1%
65.91021141
< 0.1%
65.8399581
< 0.1%
64.36854351
< 0.1%
64.25662341
< 0.1%
62.82687561
< 0.1%
62.67328881
< 0.1%
61.28520771
< 0.1%

TSS_entrada
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8716
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean452.5378587
Minimum150.229999
Maximum1033.516438
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-04-20T14:44:18.785120image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum150.229999
5-th percentile267.3333144
Q1369.48999
median458.9655437
Q3529.3317744
95-th percentile633.7488198
Maximum1033.516438
Range883.2864387
Interquartile range (IQR)159.8417844

Descriptive statistics

Standard deviation118.0140342
Coefficient of variation (CV)0.2607826769
Kurtosis0.4426866039
Mean452.5378587
Median Absolute Deviation (MAD)78.9897685
Skewness0.1035101054
Sum3964684.18
Variance13927.31227
MonotonicityNot monotonic
2023-04-20T14:44:19.420327image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52124
 
0.3%
37023
 
0.3%
473.62091971
 
< 0.1%
472.7459191
 
< 0.1%
471.87091831
 
< 0.1%
470.98269841
 
< 0.1%
467.27436211
 
< 0.1%
463.56602581
 
< 0.1%
459.85768951
 
< 0.1%
430.19099911
 
< 0.1%
Other values (8706)8706
99.4%
ValueCountFrequency (%)
150.2299991
< 0.1%
154.5563211
< 0.1%
159.1396581
< 0.1%
163.7229951
< 0.1%
166.93256921
< 0.1%
168.3063321
< 0.1%
172.8896691
< 0.1%
172.95702971
< 0.1%
173.08274941
< 0.1%
173.16608281
< 0.1%
ValueCountFrequency (%)
1033.5164381
< 0.1%
1008.4432571
< 0.1%
1001.7619361
< 0.1%
983.026571
< 0.1%
969.34524361
< 0.1%
957.6098831
< 0.1%
956.87018071
< 0.1%
950.0260681
< 0.1%
943.02606241
< 0.1%
936.9285511
< 0.1%

DQO_entrada
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8716
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean653.7216973
Minimum216.3327168
Maximum1494.300565
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-04-20T14:44:19.528592image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum216.3327168
5-th percentile385.9863472
Q1533.642636
median662.510758
Q3764.6599456
95-th percentile915.8353724
Maximum1494.300565
Range1277.967848
Interquartile range (IQR)231.0173096

Descriptive statistics

Standard deviation170.5692847
Coefficient of variation (CV)0.2609203356
Kurtosis0.4468409128
Mean653.7216973
Median Absolute Deviation (MAD)114.1051966
Skewness0.1029747242
Sum5727255.79
Variance29093.88087
MonotonicityNot monotonic
2023-04-20T14:44:19.643712image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75324
 
0.3%
53423
 
0.3%
684.61936531
 
< 0.1%
683.4110311
 
< 0.1%
682.20269671
 
< 0.1%
680.9747281
 
< 0.1%
675.5580571
 
< 0.1%
670.1413861
 
< 0.1%
664.7247151
 
< 0.1%
621.3913471
 
< 0.1%
Other values (8706)8706
99.4%
ValueCountFrequency (%)
216.33271681
< 0.1%
222.54453381
< 0.1%
229.12787241
< 0.1%
235.7112111
< 0.1%
240.51869551
< 0.1%
242.29454961
< 0.1%
245.89257421
< 0.1%
246.20687351
< 0.1%
246.4152071
< 0.1%
246.62354051
< 0.1%
ValueCountFrequency (%)
1494.3005651
< 0.1%
1458.0055671
< 0.1%
1448.42471
< 0.1%
1421.2138711
< 0.1%
1401.5913291
< 0.1%
1384.4221751
< 0.1%
1382.8125731
< 0.1%
1372.8711941
< 0.1%
1362.7045191
< 0.1%
1354.7579581
< 0.1%

DBO_entrada
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8716
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean331.8872969
Minimum109.7588131
Maximum758.455039
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-04-20T14:44:19.757083image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum109.7588131
5-th percentile195.9742129
Q1270.79
median336.4030294
Q3388.1364992
95-th percentile464.8432915
Maximum758.455039
Range648.6962259
Interquartile range (IQR)117.3464992

Descriptive statistics

Standard deviation86.58134637
Coefficient of variation (CV)0.2608757466
Kurtosis0.4452698987
Mean331.8872969
Median Absolute Deviation (MAD)57.88034341
Skewness0.1035398225
Sum2907664.608
Variance7496.329539
MonotonicityNot monotonic
2023-04-20T14:44:19.862578image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
382.3324
 
0.3%
270.7923
 
0.3%
347.39773281
 
< 0.1%
346.7775241
 
< 0.1%
346.15731521
 
< 0.1%
345.5272251
 
< 0.1%
342.78909781
 
< 0.1%
340.05097061
 
< 0.1%
337.31284341
 
< 0.1%
315.40782591
 
< 0.1%
Other values (8706)8706
99.4%
ValueCountFrequency (%)
109.75881311
< 0.1%
112.92854071
< 0.1%
116.28687671
< 0.1%
119.64521271
< 0.1%
122.02097861
< 0.1%
123.00354871
< 0.1%
125.66810391
< 0.1%
125.79068071
< 0.1%
125.87193071
< 0.1%
125.95318081
< 0.1%
ValueCountFrequency (%)
758.4550391
< 0.1%
740.03857231
< 0.1%
735.16723391
< 0.1%
721.37001571
< 0.1%
711.39346481
< 0.1%
702.70145911
< 0.1%
701.96983721
< 0.1%
696.92753681
< 0.1%
691.7708661
< 0.1%
687.61969581
< 0.1%

TN_entrada
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct8761
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.59689824
Minimum22.2453808
Maximum88.6442977
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-04-20T14:44:19.982073image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum22.2453808
5-th percentile30.19358701
Q140.89083677
median46.9246625
Q351.96068323
95-th percentile60.08271413
Maximum88.6442977
Range66.3989169
Interquartile range (IQR)11.06984647

Descriptive statistics

Standard deviation9.044455761
Coefficient of variation (CV)0.1940999531
Kurtosis0.8565921394
Mean46.59689824
Median Absolute Deviation (MAD)5.45468469
Skewness0.1767597848
Sum408235.4254
Variance81.80218001
MonotonicityNot monotonic
2023-04-20T14:44:20.101528image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.061
 
< 0.1%
53.354403751
 
< 0.1%
55.189405211
 
< 0.1%
54.883571641
 
< 0.1%
54.577738061
 
< 0.1%
54.271904481
 
< 0.1%
53.96607091
 
< 0.1%
53.660237321
 
< 0.1%
53.048570171
 
< 0.1%
51.183121951
 
< 0.1%
Other values (8751)8751
99.9%
ValueCountFrequency (%)
22.24538081
< 0.1%
22.89392131
< 0.1%
23.126445261
< 0.1%
23.520090071
< 0.1%
23.571838511
< 0.1%
23.897878161
< 0.1%
23.92759041
< 0.1%
24.00043451
< 0.1%
24.249755711
< 0.1%
24.335090721
< 0.1%
ValueCountFrequency (%)
88.64429771
< 0.1%
87.606231781
< 0.1%
86.905084951
< 0.1%
86.507064241
< 0.1%
85.854955891
< 0.1%
85.407896691
< 0.1%
85.09258351
< 0.1%
84.561104481
< 0.1%
84.308729141
< 0.1%
84.201383081
< 0.1%

TKN_entrada
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct8761
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.59689824
Minimum22.2453808
Maximum88.6442977
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-04-20T14:44:20.225753image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum22.2453808
5-th percentile30.19358701
Q140.89083677
median46.9246625
Q351.96068323
95-th percentile60.08271413
Maximum88.6442977
Range66.3989169
Interquartile range (IQR)11.06984647

Descriptive statistics

Standard deviation9.044455761
Coefficient of variation (CV)0.1940999531
Kurtosis0.8565921394
Mean46.59689824
Median Absolute Deviation (MAD)5.45468469
Skewness0.1767597848
Sum408235.4254
Variance81.80218001
MonotonicityNot monotonic
2023-04-20T14:44:20.337495image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.061
 
< 0.1%
53.354403751
 
< 0.1%
55.189405211
 
< 0.1%
54.883571641
 
< 0.1%
54.577738061
 
< 0.1%
54.271904481
 
< 0.1%
53.96607091
 
< 0.1%
53.660237321
 
< 0.1%
53.048570171
 
< 0.1%
51.183121951
 
< 0.1%
Other values (8751)8751
99.9%
ValueCountFrequency (%)
22.24538081
< 0.1%
22.89392131
< 0.1%
23.126445261
< 0.1%
23.520090071
< 0.1%
23.571838511
< 0.1%
23.897878161
< 0.1%
23.92759041
< 0.1%
24.00043451
< 0.1%
24.249755711
< 0.1%
24.335090721
< 0.1%
ValueCountFrequency (%)
88.64429771
< 0.1%
87.606231781
< 0.1%
86.905084951
< 0.1%
86.507064241
< 0.1%
85.854955891
< 0.1%
85.407896691
< 0.1%
85.09258351
< 0.1%
84.561104481
< 0.1%
84.308729141
< 0.1%
84.201383081
< 0.1%

TP_entrada
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8716
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.30915666
Minimum6.492901261
Maximum17.65388664
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-04-20T14:44:20.472074image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum6.492901261
5-th percentile7.967916425
Q19.259568421
median10.38577239
Q311.27696854
95-th percentile12.59992588
Maximum17.65388664
Range11.16098538
Interquartile range (IQR)2.017400115

Descriptive statistics

Standard deviation1.489496438
Coefficient of variation (CV)0.14448286
Kurtosis0.4489511741
Mean10.30915666
Median Absolute Deviation (MAD)0.995911994
Skewness0.1030275759
Sum90318.52148
Variance2.218599639
MonotonicityNot monotonic
2023-04-20T14:44:20.607697image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.1824
 
0.3%
9.2623
 
0.3%
10.581201421
 
< 0.1%
10.570784751
 
< 0.1%
10.560368071
 
< 0.1%
10.549778381
 
< 0.1%
10.502278351
 
< 0.1%
10.454778311
 
< 0.1%
10.407278271
 
< 0.1%
10.027277971
 
< 0.1%
Other values (8706)8706
99.4%
ValueCountFrequency (%)
6.4929012611
< 0.1%
6.5471611181
< 0.1%
6.6046611641
< 0.1%
6.662161211
< 0.1%
6.703752731
< 0.1%
6.7196612561
< 0.1%
6.7491405941
< 0.1%
6.7516549881
< 0.1%
6.7533216561
< 0.1%
6.7549883241
< 0.1%
ValueCountFrequency (%)
17.653886641
< 0.1%
17.336560561
< 0.1%
17.253087681
< 0.1%
17.014893641
< 0.1%
16.843920681
< 0.1%
16.693226721
< 0.1%
16.67836241
< 0.1%
16.591580511
< 0.1%
16.502830431
< 0.1%
16.434753691
< 0.1%

PO_entrada
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size68.6 KiB
4.6
8761 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters26283
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.6
2nd row4.6
3rd row4.6
4th row4.6
5th row4.6

Common Values

ValueCountFrequency (%)
4.68761
100.0%

Length

2023-04-20T14:44:20.714057image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-04-20T14:44:20.819896image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
4.68761
100.0%

Most occurring characters

ValueCountFrequency (%)
48761
33.3%
.8761
33.3%
68761
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number17522
66.7%
Other Punctuation8761
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
48761
50.0%
68761
50.0%
Other Punctuation
ValueCountFrequency (%)
.8761
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common26283
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
48761
33.3%
.8761
33.3%
68761
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII26283
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48761
33.3%
.8761
33.3%
68761
33.3%

Q_saida
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct8761
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7769.48742
Minimum2105.016529
Maximum13029.26465
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-04-20T14:44:20.914561image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2105.016529
5-th percentile5099.625595
Q16505.694913
median7714.114601
Q39068.469688
95-th percentile10559.85291
Maximum13029.26465
Range10924.24812
Interquartile range (IQR)2562.774775

Descriptive statistics

Standard deviation1691.466815
Coefficient of variation (CV)0.2177063586
Kurtosis-0.5203894285
Mean7769.48742
Median Absolute Deviation (MAD)1293.910001
Skewness0.04612262343
Sum68068479.28
Variance2861059.985
MonotonicityNot monotonic
2023-04-20T14:44:21.027073image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6135.0666211
 
< 0.1%
8423.8198561
 
< 0.1%
8362.8198081
 
< 0.1%
8372.9864821
 
< 0.1%
8383.1531571
 
< 0.1%
8393.3198321
 
< 0.1%
8403.4865071
 
< 0.1%
8413.6531821
 
< 0.1%
8433.9865311
 
< 0.1%
7850.6884081
 
< 0.1%
Other values (8751)8751
99.9%
ValueCountFrequency (%)
2105.0165291
< 0.1%
2291.458861
< 0.1%
2415.0422921
< 0.1%
2538.6257251
< 0.1%
2662.2091571
< 0.1%
2785.7925891
< 0.1%
2909.3760211
< 0.1%
2954.9528361
< 0.1%
3032.9594531
< 0.1%
3145.984371
< 0.1%
ValueCountFrequency (%)
13029.264651
< 0.1%
12892.259611
< 0.1%
12839.13171
< 0.1%
12751.884491
< 0.1%
12690.052011
< 0.1%
12647.131551
< 0.1%
12611.509391
< 0.1%
12589.109181
< 0.1%
12569.13751
< 0.1%
12487.60911
< 0.1%

NH_saida
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct8761
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.73046212
Minimum0.1009468957
Maximum37.50605997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-04-20T14:44:21.155708image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.1009468957
5-th percentile0.3168045368
Q11.311767555
median8.520411367
Q319.6876531
95-th percentile26.85440914
Maximum37.50605997
Range37.40511308
Interquartile range (IQR)18.37588555

Descriptive statistics

Standard deviation9.536691156
Coefficient of variation (CV)0.8887493431
Kurtosis-1.18542426
Mean10.73046212
Median Absolute Deviation (MAD)7.719497898
Skewness0.4509941639
Sum94009.5786
Variance90.94847821
MonotonicityNot monotonic
2023-04-20T14:44:21.268706image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.8549079671
 
< 0.1%
8.3764694091
 
< 0.1%
6.9004112911
 
< 0.1%
7.1411521011
 
< 0.1%
7.396952521
 
< 0.1%
7.661388161
 
< 0.1%
7.9193530231
 
< 0.1%
8.1600129271
 
< 0.1%
8.5642489341
 
< 0.1%
7.6229849431
 
< 0.1%
Other values (8751)8751
99.9%
ValueCountFrequency (%)
0.10094689571
< 0.1%
0.1065548451
< 0.1%
0.11028487811
< 0.1%
0.11403195731
< 0.1%
0.11779448611
< 0.1%
0.12157797191
< 0.1%
0.12537840481
< 0.1%
0.12920301481
< 0.1%
0.13305343421
< 0.1%
0.13693073971
< 0.1%
ValueCountFrequency (%)
37.506059971
< 0.1%
37.477242191
< 0.1%
37.439407571
< 0.1%
37.363187121
< 0.1%
37.265373531
< 0.1%
37.199200921
< 0.1%
37.146292371
< 0.1%
37.120449031
< 0.1%
37.071541251
< 0.1%
36.980762791
< 0.1%

NO_saida
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct8761
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.06466052
Minimum0.001115959022
Maximum21.54680244
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-04-20T14:44:21.408361image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.001115959022
5-th percentile0.003753363253
Q10.02237333931
median0.0954979239
Q30.3265909406
95-th percentile6.237650144
Maximum21.54680244
Range21.54568648
Interquartile range (IQR)0.3042176013

Descriptive statistics

Standard deviation3.042945271
Coefficient of variation (CV)2.858136669
Kurtosis22.78935618
Mean1.06466052
Median Absolute Deviation (MAD)0.08516944621
Skewness4.522454825
Sum9327.490812
Variance9.259515922
MonotonicityNot monotonic
2023-04-20T14:44:21.528905image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.079305879151
 
< 0.1%
0.14306202421
 
< 0.1%
0.14094639471
 
< 0.1%
0.143815321
 
< 0.1%
0.14563746841
 
< 0.1%
0.14489978291
 
< 0.1%
0.14367903221
 
< 0.1%
0.14303235591
 
< 0.1%
0.14370615941
 
< 0.1%
0.11121930581
 
< 0.1%
Other values (8751)8751
99.9%
ValueCountFrequency (%)
0.0011159590221
< 0.1%
0.0011197662961
< 0.1%
0.0011234682451
< 0.1%
0.0011271094451
< 0.1%
0.0011311001441
< 0.1%
0.0011355979291
< 0.1%
0.001136849051
< 0.1%
0.0011406226791
< 0.1%
0.0011461762191
< 0.1%
0.0011522608391
< 0.1%
ValueCountFrequency (%)
21.546802441
< 0.1%
21.545871871
< 0.1%
21.536524011
< 0.1%
21.533972511
< 0.1%
21.51469921
< 0.1%
21.511352231
< 0.1%
21.486551951
< 0.1%
21.478285031
< 0.1%
21.458161271
< 0.1%
21.435059391
< 0.1%

TSS_saida
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct8761
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.296811506
Minimum3.192627235
Maximum9.006370307
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-04-20T14:44:21.647021image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum3.192627235
5-th percentile4.416334225
Q15.356868925
median6.225303983
Q37.204800874
95-th percentile8.310579859
Maximum9.006370307
Range5.813743071
Interquartile range (IQR)1.847931949

Descriptive statistics

Standard deviation1.192987724
Coefficient of variation (CV)0.1894590179
Kurtosis-0.7997037431
Mean6.296811506
Median Absolute Deviation (MAD)0.9084465928
Skewness-0.02448952173
Sum55166.3656
Variance1.42321971
MonotonicityNot monotonic
2023-04-20T14:44:21.759997image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.6425650651
 
< 0.1%
5.8289446441
 
< 0.1%
5.7782265771
 
< 0.1%
5.7876197651
 
< 0.1%
5.7965405371
 
< 0.1%
5.8050573571
 
< 0.1%
5.8132631281
 
< 0.1%
5.8212168081
 
< 0.1%
5.8364579711
 
< 0.1%
5.9444775621
 
< 0.1%
Other values (8751)8751
99.9%
ValueCountFrequency (%)
3.1926272351
< 0.1%
3.1931212451
< 0.1%
3.1952377781
< 0.1%
3.1963841061
< 0.1%
3.2013172281
< 0.1%
3.2021374521
< 0.1%
3.2101375461
< 0.1%
3.2112904541
< 0.1%
3.2201762751
< 0.1%
3.2256815661
< 0.1%
ValueCountFrequency (%)
9.0063703071
< 0.1%
8.9908985761
< 0.1%
8.9866649011
< 0.1%
8.9584489171
< 0.1%
8.9479029931
< 0.1%
8.9289594911
< 0.1%
8.9057661951
< 0.1%
8.8991892441
< 0.1%
8.8692824561
< 0.1%
8.8644311251
< 0.1%

DQO_saida
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct8761
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.22615436
Minimum33.14232472
Maximum291.7144114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-04-20T14:44:21.882020image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum33.14232472
5-th percentile52.23744209
Q170.1120807
median84.75682774
Q3108.999282
95-th percentile184.3014412
Maximum291.7144114
Range258.5720867
Interquartile range (IQR)38.88720129

Descriptive statistics

Standard deviation38.84370285
Coefficient of variation (CV)0.4079100234
Kurtosis3.065375378
Mean95.22615436
Median Absolute Deviation (MAD)17.90441961
Skewness1.581333794
Sum834276.3384
Variance1508.833251
MonotonicityNot monotonic
2023-04-20T14:44:21.998773image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83.782369831
 
< 0.1%
86.073537231
 
< 0.1%
87.604849491
 
< 0.1%
87.438392611
 
< 0.1%
87.292510371
 
< 0.1%
87.095145421
 
< 0.1%
86.820197271
 
< 0.1%
86.475898971
 
< 0.1%
85.623268241
 
< 0.1%
81.201444751
 
< 0.1%
Other values (8751)8751
99.9%
ValueCountFrequency (%)
33.142324721
< 0.1%
33.144706711
< 0.1%
33.145931341
< 0.1%
33.153967091
< 0.1%
33.155136481
< 0.1%
33.169151381
< 0.1%
33.183656291
< 0.1%
33.187451981
< 0.1%
33.20957761
< 0.1%
33.235134521
< 0.1%
ValueCountFrequency (%)
291.71441141
< 0.1%
291.69354371
< 0.1%
291.0686941
< 0.1%
291.01851561
< 0.1%
289.74438891
< 0.1%
289.69542241
< 0.1%
287.74380451
< 0.1%
287.73478251
< 0.1%
285.17506071
< 0.1%
285.03428061
< 0.1%

DBO_saida
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct8761
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.37321752
Minimum1.527409191
Maximum96.96090807
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-04-20T14:44:22.122591image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1.527409191
5-th percentile2.009908814
Q12.793934255
median3.767286747
Q311.2250502
95-th percentile52.46078218
Maximum96.96090807
Range95.43349888
Interquartile range (IQR)8.431115947

Descriptive statistics

Standard deviation16.2806383
Coefficient of variation (CV)1.431489222
Kurtosis5.79603903
Mean11.37321752
Median Absolute Deviation (MAD)1.443234078
Skewness2.41566735
Sum99640.75874
Variance265.0591836
MonotonicityNot monotonic
2023-04-20T14:44:22.239237image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.8906408281
 
< 0.1%
4.0801965921
 
< 0.1%
3.7532806221
 
< 0.1%
3.831556871
 
< 0.1%
3.9379305181
 
< 0.1%
4.0246409121
 
< 0.1%
4.0739173451
 
< 0.1%
4.0902529021
 
< 0.1%
4.0496182271
 
< 0.1%
4.4781995161
 
< 0.1%
Other values (8751)8751
99.9%
ValueCountFrequency (%)
1.5274091911
< 0.1%
1.5276620291
< 0.1%
1.5283597571
< 0.1%
1.5292041621
< 0.1%
1.530353391
< 0.1%
1.5322229351
< 0.1%
1.5332721381
< 0.1%
1.5370191111
< 0.1%
1.5401497351
< 0.1%
1.5415120111
< 0.1%
ValueCountFrequency (%)
96.960908071
< 0.1%
96.820684261
< 0.1%
96.813452081
< 0.1%
96.398167371
< 0.1%
96.37917091
< 0.1%
95.695365611
< 0.1%
95.656456111
< 0.1%
94.811949581
< 0.1%
94.797917321
< 0.1%
94.725142611
< 0.1%

TN_saida
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct8761
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.8749935
Minimum1.610063119
Maximum38.59113715
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-04-20T14:44:22.358352image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1.610063119
5-th percentile2.262950748
Q14.244660841
median10.52522148
Q321.0677452
95-th percentile28.1149577
Maximum38.59113715
Range36.98107403
Interquartile range (IQR)16.82308436

Descriptive statistics

Standard deviation9.017956516
Coefficient of variation (CV)0.7004241608
Kurtosis-1.125856041
Mean12.8749935
Median Absolute Deviation (MAD)7.482611044
Skewness0.4266802767
Sum112797.818
Variance81.32353973
MonotonicityNot monotonic
2023-04-20T14:44:22.474570image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.0423104891
 
< 0.1%
9.6051334121
 
< 0.1%
8.1397402721
 
< 0.1%
8.3823358081
 
< 0.1%
8.6390233461
 
< 0.1%
8.9010690621
 
< 0.1%
9.1553488431
 
< 0.1%
9.3922603811
 
< 0.1%
9.7895233591
 
< 0.1%
8.777020571
 
< 0.1%
Other values (8751)8751
99.9%
ValueCountFrequency (%)
1.6100631191
< 0.1%
1.6101849541
< 0.1%
1.6137615691
< 0.1%
1.6185914061
< 0.1%
1.6208091411
< 0.1%
1.6240111981
< 0.1%
1.6298096371
< 0.1%
1.6359097091
< 0.1%
1.642275191
< 0.1%
1.6429055981
< 0.1%
ValueCountFrequency (%)
38.591137151
< 0.1%
38.570507191
< 0.1%
38.516969281
< 0.1%
38.465636591
< 0.1%
38.336074221
< 0.1%
38.318136481
< 0.1%
38.284769971
< 0.1%
38.268199561
< 0.1%
38.229605151
< 0.1%
38.149896121
< 0.1%

TKN_saida
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct8761
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.81033298
Minimum0.5416482115
Maximum38.54610649
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-04-20T14:44:22.603004image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.5416482115
5-th percentile1.054792159
Q12.350862682
median9.602633212
Q320.86496635
95-th percentile28.10354741
Maximum38.54610649
Range38.00445828
Interquartile range (IQR)18.51410367

Descriptive statistics

Standard deviation9.693187893
Coefficient of variation (CV)0.8207379005
Kurtosis-1.192732621
Mean11.81033298
Median Absolute Deviation (MAD)7.897037619
Skewness0.4407605282
Sum103470.3272
Variance93.95789153
MonotonicityNot monotonic
2023-04-20T14:44:22.735483image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.963004611
 
< 0.1%
9.4620713881
 
< 0.1%
7.9987938771
 
< 0.1%
8.2385204881
 
< 0.1%
8.4933858771
 
< 0.1%
8.7561692791
 
< 0.1%
9.0116698111
 
< 0.1%
9.2492280261
 
< 0.1%
9.6458171991
 
< 0.1%
8.6658012641
 
< 0.1%
Other values (8751)8751
99.9%
ValueCountFrequency (%)
0.54164821151
< 0.1%
0.54518598131
< 0.1%
0.54793844041
< 0.1%
0.55096784111
< 0.1%
0.55423669751
< 0.1%
0.55773431431
< 0.1%
0.56142718611
< 0.1%
0.56530120751
< 0.1%
0.56934008121
< 0.1%
0.57353003811
< 0.1%
ValueCountFrequency (%)
38.546106491
< 0.1%
38.528493631
< 0.1%
38.468690151
< 0.1%
38.426150391
< 0.1%
38.284352981
< 0.1%
38.275770631
< 0.1%
38.239429491
< 0.1%
38.227636381
< 0.1%
38.192496061
< 0.1%
38.115431051
< 0.1%

TP_saida
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct8761
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.220722091
Minimum0.09796187789
Maximum12.46458957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-04-20T14:44:22.856658image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.09796187789
5-th percentile0.1075177004
Q10.1230424331
median1.085691821
Q33.797094029
95-th percentile7.085000345
Maximum12.46458957
Range12.3666277
Interquartile range (IQR)3.674051596

Descriptive statistics

Standard deviation2.570436697
Coefficient of variation (CV)1.157477879
Kurtosis1.229236716
Mean2.220722091
Median Absolute Deviation (MAD)0.9744509244
Skewness1.260623319
Sum19455.74624
Variance6.607144811
MonotonicityNot monotonic
2023-04-20T14:44:22.975732image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.42849936651
 
< 0.1%
0.12240064751
 
< 0.1%
0.19100188371
 
< 0.1%
0.15096224941
 
< 0.1%
0.13195469031
 
< 0.1%
0.125064481
 
< 0.1%
0.12255270431
 
< 0.1%
0.12190159051
 
< 0.1%
0.12385188661
 
< 0.1%
0.11427844921
 
< 0.1%
Other values (8751)8751
99.9%
ValueCountFrequency (%)
0.097961877891
< 0.1%
0.09855935621
< 0.1%
0.098563063091
< 0.1%
0.098582400431
< 0.1%
0.098602707031
< 0.1%
0.098623139611
< 0.1%
0.098673361231
< 0.1%
0.098686412181
< 0.1%
0.098690656241
< 0.1%
0.098701184911
< 0.1%
ValueCountFrequency (%)
12.464589571
< 0.1%
12.462108331
< 0.1%
12.461479091
< 0.1%
12.455825881
< 0.1%
12.450325881
< 0.1%
12.447581221
< 0.1%
12.436786671
< 0.1%
12.429164821
< 0.1%
12.423358511
< 0.1%
12.407169111
< 0.1%

PO_saida
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct8761
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.125272021
Minimum0.003138968682
Maximum12.39692381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-04-20T14:44:23.104501image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.003138968682
5-th percentile0.003658069705
Q10.005193500586
median0.9852160231
Q33.712315176
95-th percentile7.011936287
Maximum12.39692381
Range12.39378484
Interquartile range (IQR)3.707121676

Descriptive statistics

Standard deviation2.585408441
Coefficient of variation (CV)1.216507071
Kurtosis1.197409453
Mean2.125272021
Median Absolute Deviation (MAD)0.9814025866
Skewness1.253177216
Sum18619.50818
Variance6.684336808
MonotonicityNot monotonic
2023-04-20T14:44:23.240054image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.32612372641
 
< 0.1%
0.031540213921
 
< 0.1%
0.10248712061
 
< 0.1%
0.06186328111
 
< 0.1%
0.042166911061
 
< 0.1%
0.034754162041
 
< 0.1%
0.031922521751
 
< 0.1%
0.031100642111
 
< 0.1%
0.033016789881
 
< 0.1%
0.020167558451
 
< 0.1%
Other values (8751)8751
99.9%
ValueCountFrequency (%)
0.0031389686821
< 0.1%
0.0031406003241
< 0.1%
0.0031429199451
< 0.1%
0.0031468052061
< 0.1%
0.0031494283151
< 0.1%
0.0031569936171
< 0.1%
0.0031577958441
< 0.1%
0.0031677121271
< 0.1%
0.0031718756541
< 0.1%
0.0031788156491
< 0.1%
ValueCountFrequency (%)
12.396923811
< 0.1%
12.394775661
< 0.1%
12.393515241
< 0.1%
12.387560891
< 0.1%
12.383273841
< 0.1%
12.379011671
< 0.1%
12.367909411
< 0.1%
12.362351161
< 0.1%
12.354170221
< 0.1%
12.337666651
< 0.1%

Interactions

2023-04-20T14:44:15.661016image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:38.687546image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:40.900648image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:42.799587image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:44.722918image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:46.911910image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:48.844111image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:50.880966image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:52.991216image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:55.407379image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:57.506158image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:59.588694image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:01.581511image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:03.573151image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:05.513139image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:07.854131image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:09.785711image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:11.830456image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:13.793814image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:15.763627image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:38.794523image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:41.018437image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:42.902565image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:44.827568image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:47.016428image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:48.958277image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:50.983191image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:53.097551image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:55.517130image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:57.625301image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:59.699711image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:01.680938image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:03.692751image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:05.616323image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:07.956487image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:09.890816image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:11.938227image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:13.888192image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:15.852563image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:38.928009image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:41.117884image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:42.999577image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:44.927234image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:47.125065image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:49.051125image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:51.078303image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:53.193968image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:55.613427image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:57.714539image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:59.806415image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:01.771884image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:03.798388image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:05.705962image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:08.065195image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:09.984641image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:12.043066image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:13.985945image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:15.943938image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:39.046113image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:41.214056image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:43.100713image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:45.022762image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:47.222006image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:49.155179image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:51.201054image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:53.305627image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:55.715645image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:57.816390image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:59.911355image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:01.874983image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:03.899801image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:05.807052image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:08.169753image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:10.084107image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:12.148156image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:14.079504image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:16.035360image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:39.150680image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:41.314440image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:43.198747image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:45.110936image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:47.314288image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:49.248256image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:51.303194image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:53.403591image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:55.819067image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:57.922934image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:00.012489image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:01.961882image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:03.994424image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:05.902234image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:08.257815image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:10.176605image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:12.252795image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:14.171468image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:16.130295image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:39.268403image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:41.432386image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:43.294085image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:45.206394image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:47.418937image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:49.351795image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:51.405587image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:53.507072image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:55.922706image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:43:58.031634image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-04-20T14:44:00.124390image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-04-20T14:44:15.564499image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-04-20T14:44:23.370478image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-04-20T14:44:23.608527image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-04-20T14:44:23.833076image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-04-20T14:44:24.065342image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-04-20T14:44:17.597089image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.

Sample

First rows

DiaQ_entradaNH_entradaTSS_entradaDQO_entradaDBO_entradaTN_entradaTKN_entradaTP_entradaPO_entradaQ_saidaNH_saidaNO_saidaTSS_saidaDQO_saidaDBO_saidaTN_saidaTKN_saidaTP_saidaPO_saida
00.0000004719.00000021.000000271.000000391.000000198.57500034.06000034.0600008.0200004.66135.0666213.8549080.0793066.64256583.7823703.8906415.0423104.9630050.4284990.326124
10.0416674766.83337221.041667271.208333391.375000198.76187534.11416734.1141678.0233334.64536.5463663.1104030.2350106.47522681.4851613.1574724.4274494.1924390.6714960.573663
20.0833334814.66674321.083333271.416667391.750001198.94875034.16833334.1683338.0266674.64584.3797432.4292800.2853686.32846580.1563423.0323213.7773123.4919441.1251221.029731
30.1250004862.50011521.125000271.625000392.125001199.13562534.22250034.2225008.0300004.64632.2131151.8584920.2985686.24502278.9860792.9772023.2043242.9057571.6264301.532398
40.1666674910.33348621.166667271.833334392.500001199.32250134.27666734.2766678.0333334.64680.0464861.4176330.2986876.19365777.8948452.9467272.7502412.4515552.1506522.057467
50.2083344958.16685821.208333272.041668392.875001199.50937634.33083434.3308348.0366674.64727.8798581.1140030.2926346.15667776.8571942.9280302.4283082.1356742.6891612.596591
60.2500005006.00023021.250000272.250001393.250002199.69625134.38500034.3850008.0400004.64775.7132300.9319920.2845496.12636775.8623282.9158652.2266231.9420743.2312753.139217
70.2916675053.83360121.291667272.458334393.625002199.88312634.43916734.4391678.0433334.64823.5466010.8363620.2779686.09932474.9028762.9064172.1132691.8353013.7633843.671795
80.3333345101.66697321.333334272.666668394.000002200.07000134.49333434.4933348.0466674.64871.3799730.7907860.2745506.07406673.9738902.8970812.0534821.7789324.2746864.183554
90.3750005149.50034421.375000272.875001394.375003200.25687634.54750034.5475008.0500004.64919.2133440.7703990.2741046.04995373.0731872.8868782.0221751.7480704.7598964.669214

Last rows

DiaQ_entradaNH_entradaTSS_entradaDQO_entradaDBO_entradaTN_entradaTKN_entradaTP_entradaPO_entradaQ_saidaNH_saidaNO_saidaTSS_saidaDQO_saidaDBO_saidaTN_saidaTKN_saidaTP_saidaPO_saida
8751364.6252923509.38481817.374708173.749417247.873541126.44068125.65091525.6509156.7649884.63280.1263180.13693118.5602653.19262733.2351351.54151219.1337950.5735305.3275365.282815
8752364.6669583385.80138617.333042173.666083247.665208126.35943125.60299825.6029986.7633224.63156.5428860.13305318.6053353.19523833.2095781.53701919.1746750.5693405.3837375.339028
8753364.7086253262.21795317.291375173.582750247.456874126.27818125.55508125.5550816.7616554.63032.9594530.12920318.6522683.20131733.1874521.53327219.2175700.5653015.4414835.396749
8754364.7502923138.63452117.249708173.499416247.248541126.19693125.50716425.5071646.7599884.62909.3760210.12537818.7010933.21129033.1691511.53035319.2625200.5614275.5008185.456017
8755364.7919593015.05108917.208041173.416083247.040207126.11568125.45924825.4592486.7583224.62785.7925890.12157818.7518283.22568233.1551361.52836019.3095620.5577345.5617745.516860
8756364.8336252891.46765717.166375173.332750246.831874126.03443125.41133125.4113316.7566554.62662.2091570.11779418.8044703.24510033.1459311.52740919.3587070.5542375.6243985.579316
8757364.8752922767.88422517.124708173.249416246.623540125.95318125.36341425.3634146.7549884.62538.6257250.11403218.8591353.27041233.1423251.52766219.4101030.5509685.6887795.643467
8758364.9169592644.30079217.083041173.166083246.415207125.87193125.31549825.3154986.7533224.62415.0422920.11028518.9157393.30209333.1447071.52920419.4636770.5479385.7548755.709267
8759364.9586252520.71736017.041375173.082749246.206873125.79068125.26758125.2675816.7516554.62291.4588600.10655518.9742513.34124633.1539671.53222319.5194370.5451865.8226565.776672
8760365.0000002334.27502916.978515172.957030245.892574125.66810425.19529225.1952926.7491414.62105.0165290.10094719.0666663.41739633.1836561.54015019.6083140.5416485.9286735.881943